history of the u.s market Safe Withdrawal Rates

Bill Sharpe And The Challenge Of Retirement Planning

Nobel laureate Bill Sharpe has focused the most recent phase of his career on retirement income analysis. A big part of that work has been the creation of a 21-chapter online financial economics textbook covering the various subject areas related to retirement planning. What I found most interesting in studying the text were Sharpe’s observations about current retirement planning practices – especially his skepticism about products and approaches used by advisors. I’ll provide a review of the text, plus additional comments on these views.

Q1 hedge fund letters, conference, scoops etc

history of the u.s market Safe Withdrawal Rates

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The textbook is titled RISMAT, which stands for Retirement Income Analysis (with scenario matrices). It covers subjects within the scope of financial economics such as utility theory and the valuation of assets, and others from outside such as demographics and building mortality tables. Incorporated in the text is a comprehensive system for retirement planning developed by Sharpe using Matlab software. I was surprised to discover that Sharpe is not a retired professor who dabbles in computer programming, but a skilled and experienced programmer capable of building large, integrated and full-featured systems.

For those familiar with Matlab, Sharpe’s textbook chapters show the development of the Matlab code for the various modules in the system. To run the system, one has to have access to Matlab software (requiring a pricey subscription) and experience working with Matlab. However, for non-Matlab users, the text provides an easy-to-understand descriptions of the program structure.

Sharpe’s general approach to retirement planning analysis reflects his extensive knowledge of financial economics and provides a quite different perspective than the research one encounters in publications that planners and advisors will be most familiar with, such as the Journal of Financial Planning. The good news is that Sharpe offers a refreshing new way of looking at retirement planning analysis; the bad news is that reading his textbook can be challenging, particularly for those like me without a strong background in financial economics. But, based on my own experience studying the text, it’s well worth the effort – even if a few of the chapters turn out to be difficult. I found the text to be a pleasure to read; Sharpe is articulate and precise in his language, but also displays a wry sense of humor.

To learn more about Sharpe’s research and ideas, I also recommend Michael Finke’s February 2019 article in Investment Advisor. An older piece I recommend is the 2014 Advisor Perspectives interview Sharpe did with Bob Huebscher.

Software structure

The “Mat” in Matlab refers to matrix, and RISMAT is built on a matrix structure. One can think in terms of a matrix with 100,000 rows, each representing a separate retirement scenario, and 60 or so columns representing the year-by-year progression of retirement to the maximum possible length. Typically each of the rows would be generated by some type of Monte Carlo process.

Throughout the text, Sharpe uses an example of a retired couple, Bob 67 and Sue 65. The first matrix he builds is a personal states matrix for the couple. The personal states are: both alive, Sue only alive, Bob only alive, and both deceased. These personal states are generated year-by-year for each row of the matrix by applying random number generation to a mortality table to appropriately recognize the random nature of longevity. We end up with 100,000 rows (scenarios) with different longevity characteristics in terms of the length of retirement with both alive, followed by differences in who survives and for how long.

The retirement modeling then operates by combining matrices. For example, let’s say Bob and Sue begin retirement with $1 million, invest all their savings in a 60/40 stock/bond portfolio, and take annual withdrawals based on required minimum distributions (RMDs). RISMAT will generate 100,000-row matrices of year-by-year investment returns using Monte Carlo techniques and the RMD withdrawal percentages will be applied to year-by year savings balances. The personal states and investment matrices will be combined to produce a matrix that shows, for each of the 100,000 scenarios, year-by-year withdrawals and bequests after the last member of the couple dies. If there are investment or advisor fees, these will also be built into a matrix so that the scenario matrices can be generated both before and after fees.

Read the full article here by Joe Tomlinson, Advisor Perspectives

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